Lecture-4 Bayesian Inference: Hierarchical Bayes models and Gibbs Sampler скачать в хорошем качестве

Lecture-4 Bayesian Inference: Hierarchical Bayes models and Gibbs Sampler

📘 Lecture 4: Bayesian Inference – Hierarchical Bayes Models and Gibbs Sampler In this lecture, we explore Hierarchical Bayesian Models and the Gibbs Sampling algorithm, two powerful tools for modeling complex data and performing Bayesian computation. You will learn: ✔️ Concept of hierarchical (multi-level) Bayesian models ✔️ Motivation and advantages of hierarchical modeling ✔️ Structure of prior, likelihood, and hyperprior distributions ✔️ Introduction to Markov Chain Monte Carlo (MCMC) methods ✔️ Gibbs Sampler: theory, algorithm, and implementation ✔️ Conditional posterior distributions ✔️ Convergence diagnostics and practical considerations ✔️ Implementation using R and real-data examples ✔️ Applications in: • Medical and biological studies • Social sciences and survey analysis • Reliability and survival analysis • Machine learning and big data analytics This lecture is ideal for undergraduate and postgraduate students, research scholars, and professionals interested in Bayesian statistics, computational methods, and applied modeling. 📌 By the end of this session, you will be able to: Build and interpret hierarchical Bayesian models Apply Gibbs sampling for posterior inference Understand MCMC convergence and efficiency Implement Bayesian hierarchical models in practice 📚 Recommended for: Statistics | Data Science | Bayesian Modeling | Computational Statistics | Research Methods 🔔 Don’t forget to like, share, and subscribe for more lectures on Bayesian Inference and Advanced Statistical Methods. 📩 For academic queries and collaborations, feel free to connect. #BayesianInference #HierarchicalBayes #GibbsSampler #MCMC #StatisticsLecture #DataScience #BayesianModeling #ResearchScholar